Application-Level Network Monitoring Through Home–Operator Data Linking
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Solution Overview
Problem
Existing network performance monitoring methods fail to provide comprehensive, user-centric analysis of application-level services, failing to identify the source of network lag or buffering issues effectively.
Innovation Solution
A method for monitoring network performance by obtaining and linking first and second monitoring data from both the home and operator networks, enabling end-to-end performance analysis and allowing for corrective actions to be taken in both networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional network monitoring methods are used, then network performance data can be collected, but comprehensive user-centric analysis of application-level services cannot be achieved
Solution Approach 1:
The patent combines monitoring data from multiple sources (home network devices, operator network, and application-level services) into a unified monitoring system. This merging enables comprehensive end-to-end performance analysis while maintaining manageable complexity through integrated data collection and correlation mechanisms.
Solution Approach 2:
The monitoring system is designed to universally collect and analyze various types of performance data across different network domains and application services. This multi-functional approach allows the same system to handle diverse monitoring requirements without requiring separate specialized systems for each function.
2Reliability
If end-to-end performance monitoring is implemented, then the source of network lag can be identified, but real-time data collection and analysis becomes more complex
Solution Approach 1:
The patent segments the monitoring system into distinct functional components: data collection modules at different network levels, data correlation engines, and analysis modules. This segmentation allows real-time end-to-end monitoring to be implemented through coordinated simple components rather than a single complex system, improving reliability while managing complexity.
Solution Approach 2:
The system implements feedback mechanisms where performance data from application-level services is continuously monitored and fed back to adjust monitoring parameters and trigger corrective actions. This feedback loop enables automatic adaptation to changing network conditions, improving service reliability without requiring manual intervention or overly complex analysis systems.
3Measurement precision
If application-level service monitoring is added to traditional network monitoring, then user-centric performance analysis is achieved, but the monitoring scope and data volume increase
Solution Approach 1:
The patent extracts only the essential performance metrics and key data elements from application-level services that are relevant to user experience analysis. Rather than collecting all possible data, the system selectively extracts critical performance indicators, reducing data volume while maintaining measurement precision for user-centric analysis.
Solution Approach 2:
The system implements partial monitoring of application services by focusing on specific critical performance aspects rather than comprehensive monitoring of all service parameters. This selective approach achieves sufficient user experience measurement accuracy without generating excessive data volumes that would overwhelm the monitoring system.
Data Source
AI summary
Monitoring network performance relating to an application-level service used by a user by obtaining first monitoring data measuring performance of a home network (11) of the user concerning the application-level service and/or type of a device used by the user, obtaining second monitoring data measuring performance of an operator network (20-40) concerning said application-level service and/or type of a device used by said user, linking the first monitoring data with the second monitoring data so as to obtain combined application-level and/or device type-level monitoring data concerning said particular service used by said particular user, and storing said combined application-level and/or device type-level monitoring data at a data lake (72).